{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 10: Risk Parity Portfolio Optimization\n",
    "\n",
    "## 1. Downloading the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  25 of 25 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "\n",
    "yf.pdr_override()\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2016-01-01'\n",
    "end = '2019-12-30'\n",
    "\n",
    "# Tickers of assets\n",
    "assets = ['JCI', 'TGT', 'CMCSA', 'CPB', 'MO', 'APA', 'MMC', 'JPM',\n",
    "          'ZION', 'PSA', 'BAX', 'BMY', 'LUV', 'PCAR', 'TXT', 'TMO',\n",
    "          'DE', 'MSFT', 'HPQ', 'SEE', 'VZ', 'CNP', 'NI', 'T', 'BA']\n",
    "assets.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(assets, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-2.0257%</td>\n",
       "      <td>0.4057%</td>\n",
       "      <td>0.4036%</td>\n",
       "      <td>1.9693%</td>\n",
       "      <td>0.0180%</td>\n",
       "      <td>0.9305%</td>\n",
       "      <td>0.3678%</td>\n",
       "      <td>0.5783%</td>\n",
       "      <td>0.9483%</td>\n",
       "      <td>-1.1953%</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5881%</td>\n",
       "      <td>0.0212%</td>\n",
       "      <td>2.8236%</td>\n",
       "      <td>0.9758%</td>\n",
       "      <td>0.6987%</td>\n",
       "      <td>1.7539%</td>\n",
       "      <td>-0.1730%</td>\n",
       "      <td>0.2409%</td>\n",
       "      <td>1.3735%</td>\n",
       "      <td>-1.0857%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-11.4863%</td>\n",
       "      <td>-1.5879%</td>\n",
       "      <td>0.2411%</td>\n",
       "      <td>-1.7557%</td>\n",
       "      <td>-0.7727%</td>\n",
       "      <td>-1.2473%</td>\n",
       "      <td>-0.1736%</td>\n",
       "      <td>-1.1239%</td>\n",
       "      <td>-3.5867%</td>\n",
       "      <td>-0.9551%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5547%</td>\n",
       "      <td>0.0212%</td>\n",
       "      <td>0.1592%</td>\n",
       "      <td>-1.5647%</td>\n",
       "      <td>-0.1466%</td>\n",
       "      <td>-1.0155%</td>\n",
       "      <td>-0.7653%</td>\n",
       "      <td>-3.0048%</td>\n",
       "      <td>-0.9035%</td>\n",
       "      <td>-2.9145%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-5.1389%</td>\n",
       "      <td>-4.1922%</td>\n",
       "      <td>-1.6573%</td>\n",
       "      <td>-2.7699%</td>\n",
       "      <td>-1.1047%</td>\n",
       "      <td>-1.9769%</td>\n",
       "      <td>-1.2207%</td>\n",
       "      <td>-0.8855%</td>\n",
       "      <td>-4.6059%</td>\n",
       "      <td>-2.5394%</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.2066%</td>\n",
       "      <td>-3.0309%</td>\n",
       "      <td>-1.0411%</td>\n",
       "      <td>-3.1557%</td>\n",
       "      <td>-1.6148%</td>\n",
       "      <td>-0.2700%</td>\n",
       "      <td>-2.2845%</td>\n",
       "      <td>-2.0570%</td>\n",
       "      <td>-0.5492%</td>\n",
       "      <td>-3.0019%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>0.2737%</td>\n",
       "      <td>-2.2705%</td>\n",
       "      <td>-1.6037%</td>\n",
       "      <td>-2.5425%</td>\n",
       "      <td>0.1099%</td>\n",
       "      <td>-0.2241%</td>\n",
       "      <td>0.5706%</td>\n",
       "      <td>-1.6402%</td>\n",
       "      <td>-1.7641%</td>\n",
       "      <td>-0.1649%</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.1539%</td>\n",
       "      <td>-1.1366%</td>\n",
       "      <td>-0.7308%</td>\n",
       "      <td>-0.1448%</td>\n",
       "      <td>0.0895%</td>\n",
       "      <td>-3.3839%</td>\n",
       "      <td>-0.1117%</td>\n",
       "      <td>-1.1387%</td>\n",
       "      <td>-0.9719%</td>\n",
       "      <td>-1.1254%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>-4.3384%</td>\n",
       "      <td>0.1693%</td>\n",
       "      <td>-1.6851%</td>\n",
       "      <td>-1.0215%</td>\n",
       "      <td>0.0915%</td>\n",
       "      <td>-1.1791%</td>\n",
       "      <td>0.5674%</td>\n",
       "      <td>0.5287%</td>\n",
       "      <td>0.6616%</td>\n",
       "      <td>0.0331%</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6436%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.9869%</td>\n",
       "      <td>-0.1450%</td>\n",
       "      <td>1.2224%</td>\n",
       "      <td>1.4570%</td>\n",
       "      <td>0.5367%</td>\n",
       "      <td>-0.4607%</td>\n",
       "      <td>0.5800%</td>\n",
       "      <td>-1.9919%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 APA       BA      BAX      BMY    CMCSA      CNP      CPB  \\\n",
       "Date                                                                         \n",
       "2016-01-05  -2.0257%  0.4057%  0.4036%  1.9693%  0.0180%  0.9305%  0.3678%   \n",
       "2016-01-06 -11.4863% -1.5879%  0.2411% -1.7557% -0.7727% -1.2473% -0.1736%   \n",
       "2016-01-07  -5.1389% -4.1922% -1.6573% -2.7699% -1.1047% -1.9769% -1.2207%   \n",
       "2016-01-08   0.2737% -2.2705% -1.6037% -2.5425%  0.1099% -0.2241%  0.5706%   \n",
       "2016-01-11  -4.3384%  0.1693% -1.6851% -1.0215%  0.0915% -1.1791%  0.5674%   \n",
       "\n",
       "                 DE      HPQ      JCI  ...       NI     PCAR      PSA  \\\n",
       "Date                                   ...                              \n",
       "2016-01-05  0.5783%  0.9483% -1.1953%  ...  1.5881%  0.0212%  2.8236%   \n",
       "2016-01-06 -1.1239% -3.5867% -0.9551%  ...  0.5547%  0.0212%  0.1592%   \n",
       "2016-01-07 -0.8855% -4.6059% -2.5394%  ... -2.2066% -3.0309% -1.0411%   \n",
       "2016-01-08 -1.6402% -1.7641% -0.1649%  ... -0.1539% -1.1366% -0.7308%   \n",
       "2016-01-11  0.5287%  0.6616%  0.0331%  ...  1.6436%  0.0000%  0.9869%   \n",
       "\n",
       "                SEE        T      TGT      TMO      TXT       VZ     ZION  \n",
       "Date                                                                       \n",
       "2016-01-05  0.9758%  0.6987%  1.7539% -0.1730%  0.2409%  1.3735% -1.0857%  \n",
       "2016-01-06 -1.5647% -0.1466% -1.0155% -0.7653% -3.0048% -0.9035% -2.9145%  \n",
       "2016-01-07 -3.1557% -1.6148% -0.2700% -2.2845% -2.0570% -0.5492% -3.0019%  \n",
       "2016-01-08 -0.1448%  0.0895% -3.3839% -0.1117% -1.1387% -0.9719% -1.1254%  \n",
       "2016-01-11 -0.1450%  1.2224%  1.4570%  0.5367% -0.4607%  0.5800% -1.9919%  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating returns\n",
    "\n",
    "Y = data[assets].pct_change().dropna()\n",
    "\n",
    "display(Y.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Estimating Mean Variance Portfolios\n",
    "\n",
    "### 2.1 Calculating the portfolio that maximizes Sharpe ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.1590%</td>\n",
       "      <td>11.5018%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.4808%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.8194%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>10.8262%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.1805%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.2740%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA      BAX     BMY   CMCSA     CNP     CPB      DE  \\\n",
       "weights 0.0000% 6.1590% 11.5018% 0.0000% 0.0000% 8.4808% 0.0000% 3.8194%   \n",
       "\n",
       "            HPQ     JCI  ...       NI    PCAR     PSA     SEE       T     TGT  \\\n",
       "weights 0.0000% 0.0000%  ... 10.8262% 0.0000% 0.0000% 0.0000% 0.0000% 7.1805%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 0.0000% 0.0000% 4.2740% 0.0000%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.Portfolio as pf\n",
    "\n",
    "# Building the portfolio object\n",
    "port = pf.Portfolio(returns=Y)\n",
    "# Calculating optimum portfolio\n",
    "\n",
    "# Select method and estimate input parameters:\n",
    "\n",
    "method_mu='hist' # Method to estimate expected returns based on historical data.\n",
    "method_cov='hist' # Method to estimate covariance matrix based on historical data.\n",
    "\n",
    "port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "model='Classic' # Could be Classic (historical), BL (Black Litterman) or FM (Factor Model)\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
    "hist = True # Use historical scenarios for risk measures that depend on scenarios\n",
    "rf = 0 # Risk free rate\n",
    "l = 0 # Risk aversion factor, only useful when obj is 'Utility'\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.PlotFunctions as plf\n",
    "\n",
    "# Plotting the composition of the portfolio\n",
    "\n",
    "ax = plf.plot_pie(w=w, title='Sharpe Mean Variance', others=0.05, nrow=25, cmap = \"tab20\",\n",
    "                  height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Plotting Risk Composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the risk composition of the portfolio\n",
    "\n",
    "ax = plf.plot_risk_con(w, cov=port.cov, returns=port.returns, rm=rm, rf=0, alpha=0.01,\n",
    "                       color=\"tab:blue\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Estimating Risk Parity Portfolios for Other Risk Measures\n",
    "\n",
    "In this part I will calculate risk parity portfolios. First I'm going to calculate risk parity portfolio when we use variance as risk measure, then I'm going to calculate the risk parity portfolios for all available risk measures.\n",
    "\n",
    "### 3.1 Calculating the risk parity portfolio for variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.1590%</td>\n",
       "      <td>11.5018%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>8.4808%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.8194%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>10.8262%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.1805%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.2740%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA      BAX     BMY   CMCSA     CNP     CPB      DE  \\\n",
       "weights 0.0000% 6.1590% 11.5018% 0.0000% 0.0000% 8.4808% 0.0000% 3.8194%   \n",
       "\n",
       "            HPQ     JCI  ...       NI    PCAR     PSA     SEE       T     TGT  \\\n",
       "weights 0.0000% 0.0000%  ... 10.8262% 0.0000% 0.0000% 0.0000% 0.0000% 7.1805%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 0.0000% 0.0000% 4.2740% 0.0000%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = None # Risk contribution constraints vector\n",
    "\n",
    "w_rp = port.rp_optimization(model=model, rm=rm, rf=rf, b=b, hist=hist)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plf.plot_pie(w=w_rp, title='Risk Parity Variance', others=0.05, nrow=25, cmap = \"tab20\",\n",
    "                  height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3  Plotting Risk Composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plf.plot_risk_con(w_rp, cov=port.cov, returns=port.returns, rm=rm, rf=0, alpha=0.01,\n",
    "                       color=\"tab:blue\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Calculate Optimal Portfolios for Several Risk Measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/danycajas/opt/anaconda3/lib/python3.8/site-packages/cvxpy/problems/problem.py:1060: UserWarning: Solution may be inaccurate. Try another solver, adjusting the solver settings, or solve with verbose=True for more information.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Risk Measures available:\n",
    "#\n",
    "# 'MV': Standard Deviation.\n",
    "# 'MAD': Mean Absolute Deviation.\n",
    "# 'MSV': Semi Standard Deviation.\n",
    "# 'FLPM': First Lower Partial Moment (Omega Ratio).\n",
    "# 'SLPM': Second Lower Partial Moment (Sortino Ratio).\n",
    "# 'CVaR': Conditional Value at Risk.\n",
    "# 'EVaR': Entropic Value at Risk.\n",
    "# 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'UCI': Ulcer Index of uncompounded cumulative returns.\n",
    "\n",
    "rms = ['MV', 'MAD', 'MSV', 'FLPM', 'SLPM', 'CVaR',\n",
    "       'EVaR', 'CDaR', 'UCI', 'EDaR']\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "\n",
    "for i in rms:\n",
    "    w = port.rp_optimization(model=model, rm=i, rf=rf, b=b, hist=hist)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        }#T_bbcd6_row1_col6{\n",
       "            background-color:  #feffe1;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row1_col7,#T_bbcd6_row12_col4{\n",
       "            background-color:  #95d385;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row1_col8{\n",
       "            background-color:  #7cc87b;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row1_col9,#T_bbcd6_row21_col7{\n",
       "            background-color:  #d0ec9f;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row2_col0,#T_bbcd6_row12_col7{\n",
       "            background-color:  #b1df90;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row2_col1,#T_bbcd6_row13_col2{\n",
       "            background-color:  #70c275;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row2_col2{\n",
       "            background-color:  #c8e99b;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row2_col3,#T_bbcd6_row7_col9{\n",
       "            background-color:  #6bc072;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row2_col4,#T_bbcd6_row21_col3{\n",
       "            background-color:  #b9e294;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row2_col5,#T_bbcd6_row18_col0{\n",
       "            background-color:  #d9f0a3;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row2_col6,#T_bbcd6_row14_col2,#T_bbcd6_row16_col5{\n",
       "            background-color:  #eff9b3;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row2_col7,#T_bbcd6_row4_col7,#T_bbcd6_row22_col0,#T_bbcd6_row24_col9{\n",
       "            background-color:  #f7fcba;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row2_col8,#T_bbcd6_row24_col1{\n",
       "            background-color:  #ddf2a6;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row2_col9,#T_bbcd6_row7_col0,#T_bbcd6_row8_col8,#T_bbcd6_row16_col1{\n",
       "            background-color:  #f3fab6;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row3_col0{\n",
       "            background-color:  #a9db8c;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row3_col1{\n",
       "            background-color:  #83cb7d;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row3_col2,#T_bbcd6_row3_col4,#T_bbcd6_row20_col0{\n",
       "            background-color:  #b5e092;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row3_col3,#T_bbcd6_row20_col4{\n",
       "            background-color:  #9fd788;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row3_col5{\n",
       "            background-color:  #d6efa2;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row3_col6{\n",
       "            background-color:  #a6da8b;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row3_col7,#T_bbcd6_row11_col7,#T_bbcd6_row11_col8,#T_bbcd6_row18_col8,#T_bbcd6_row20_col7{\n",
       "            background-color:  #fbfdce;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row3_col8,#T_bbcd6_row8_col4,#T_bbcd6_row22_col8{\n",
       "            background-color:  #fcfed6;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row3_col9,#T_bbcd6_row14_col5,#T_bbcd6_row16_col9{\n",
       "            background-color:  #f8fdc1;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row4_col0,#T_bbcd6_row20_col1{\n",
       "            background-color:  #bce395;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row4_col1,#T_bbcd6_row7_col8{\n",
       "            background-color:  #98d486;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row4_col2{\n",
       "            background-color:  #ccea9d;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row4_col3{\n",
       "            background-color:  #afde8f;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row4_col4{\n",
       "            background-color:  #c3e698;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row4_col6{\n",
       "            background-color:  #eef9b3;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row4_col8,#T_bbcd6_row6_col7,#T_bbcd6_row6_col8,#T_bbcd6_row6_col9,#T_bbcd6_row16_col4{\n",
       "            background-color:  #f4fbb7;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row4_col9,#T_bbcd6_row11_col3,#T_bbcd6_row11_col6{\n",
       "            background-color:  #e0f3a8;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row5_col0{\n",
       "            background-color:  #40aa5c;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row5_col1{\n",
       "            background-color:  #349a52;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row5_col2{\n",
       "            background-color:  #5ab76a;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row5_col3,#T_bbcd6_row13_col0,#T_bbcd6_row23_col0{\n",
       "            background-color:  #42ab5d;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row5_col4{\n",
       "            background-color:  #43ac5e;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row5_col5{\n",
       "            background-color:  #53b466;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row5_col6{\n",
       "            background-color:  #5fba6c;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row5_col7,#T_bbcd6_row6_col3{\n",
       "            background-color:  #79c679;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row5_col8{\n",
       "            background-color:  #88cd7f;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row5_col9{\n",
       "            background-color:  #2e924c;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row6_col0,#T_bbcd6_row12_col3,#T_bbcd6_row15_col8{\n",
       "            background-color:  #4ab062;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row6_col1{\n",
       "            background-color:  #64bc6f;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row6_col2{\n",
       "            background-color:  #379e54;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row6_col4{\n",
       "            background-color:  #31974f;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row6_col5,#T_bbcd6_row14_col8{\n",
       "            background-color:  #10743c;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_bbcd6_row6_col6,#T_bbcd6_row15_col1,#T_bbcd6_row15_col3,#T_bbcd6_row17_col0,#T_bbcd6_row17_col2,#T_bbcd6_row17_col4,#T_bbcd6_row17_col5,#T_bbcd6_row17_col7,#T_bbcd6_row17_col8,#T_bbcd6_row17_col9{\n",
       "            background-color:  #004529;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_bbcd6_row7_col1,#T_bbcd6_row14_col0{\n",
       "            background-color:  #e7f6ad;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row7_col2,#T_bbcd6_row22_col6{\n",
       "            background-color:  #fafdc8;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row7_col3,#T_bbcd6_row10_col0,#T_bbcd6_row10_col4,#T_bbcd6_row21_col4{\n",
       "            background-color:  #e2f4aa;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row7_col4,#T_bbcd6_row8_col7,#T_bbcd6_row9_col6,#T_bbcd6_row18_col9{\n",
       "            background-color:  #f8fcbd;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row7_col5,#T_bbcd6_row10_col6,#T_bbcd6_row16_col8,#T_bbcd6_row22_col4{\n",
       "            background-color:  #fafdcb;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row7_col6,#T_bbcd6_row22_col5{\n",
       "            background-color:  #fcfed3;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row7_col7,#T_bbcd6_row12_col6,#T_bbcd6_row21_col1{\n",
       "            background-color:  #bde496;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row8_col0,#T_bbcd6_row13_col9,#T_bbcd6_row24_col4{\n",
       "            background-color:  #f9fdc4;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row8_col1,#T_bbcd6_row11_col2,#T_bbcd6_row19_col7{\n",
       "            background-color:  #ecf7b1;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row8_col2,#T_bbcd6_row22_col9,#T_bbcd6_row24_col5{\n",
       "            background-color:  #fdfedb;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row8_col3,#T_bbcd6_row10_col2,#T_bbcd6_row18_col4,#T_bbcd6_row18_col6,#T_bbcd6_row21_col2{\n",
       "            background-color:  #ebf7b0;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row8_col9{\n",
       "            background-color:  #f7fcbc;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row9_col0,#T_bbcd6_row11_col1{\n",
       "            background-color:  #d2eda0;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row9_col1,#T_bbcd6_row12_col5{\n",
       "            background-color:  #c5e89a;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row9_col2,#T_bbcd6_row11_col0,#T_bbcd6_row21_col0{\n",
       "            background-color:  #def2a7;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row9_col3,#T_bbcd6_row14_col9,#T_bbcd6_row24_col8{\n",
       "            background-color:  #c9e99c;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row9_col4{\n",
       "            background-color:  #daf0a4;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row9_col5,#T_bbcd6_row18_col2{\n",
       "            background-color:  #eaf7af;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row9_col7,#T_bbcd6_row11_col9,#T_bbcd6_row16_col7,#T_bbcd6_row20_col9{\n",
       "            background-color:  #fbfed0;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row9_col9{\n",
       "            background-color:  #fafdc9;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row10_col1,#T_bbcd6_row19_col5{\n",
       "            background-color:  #b8e293;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row10_col3,#T_bbcd6_row20_col3{\n",
       "            background-color:  #aedd8e;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row10_col5,#T_bbcd6_row16_col2,#T_bbcd6_row20_col8,#T_bbcd6_row21_col6{\n",
       "            background-color:  #f8fcbe;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row10_col7{\n",
       "            background-color:  #e4f4ab;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row10_col8{\n",
       "            background-color:  #d5eea1;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row10_col9,#T_bbcd6_row11_col4{\n",
       "            background-color:  #e9f6af;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row12_col0{\n",
       "            background-color:  #8ed082;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row12_col1{\n",
       "            background-color:  #55b567;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row12_col2,#T_bbcd6_row15_col7{\n",
       "            background-color:  #abdc8d;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row12_col8{\n",
       "            background-color:  #62bb6e;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row12_col9,#T_bbcd6_row20_col2{\n",
       "            background-color:  #b6e192;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row13_col1{\n",
       "            background-color:  #389f55;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row13_col3{\n",
       "            background-color:  #5cb86b;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row13_col4{\n",
       "            background-color:  #72c376;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row13_col5{\n",
       "            background-color:  #84cb7e;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row13_col6,#T_bbcd6_row19_col6{\n",
       "            background-color:  #d3eda0;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row13_col7,#T_bbcd6_row14_col6{\n",
       "            background-color:  #f9fdc5;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row14_col1,#T_bbcd6_row19_col2{\n",
       "            background-color:  #a4d98a;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row14_col3{\n",
       "            background-color:  #8dcf81;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row14_col4{\n",
       "            background-color:  #e5f5ac;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row14_col7{\n",
       "            background-color:  #a2d88a;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row15_col0{\n",
       "            background-color:  #005931;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_bbcd6_row15_col2{\n",
       "            background-color:  #036b38;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_bbcd6_row15_col4{\n",
       "            background-color:  #005a31;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_bbcd6_row15_col5{\n",
       "            background-color:  #1d7f41;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_bbcd6_row15_col6{\n",
       "            background-color:  #268846;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row15_col9{\n",
       "            background-color:  #a7db8c;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row16_col0,#T_bbcd6_row22_col3{\n",
       "            background-color:  #f6fcb8;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row16_col3{\n",
       "            background-color:  #f0f9b4;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row16_col6{\n",
       "            background-color:  #f8fcc0;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row17_col1{\n",
       "            background-color:  #00502d;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_bbcd6_row17_col3{\n",
       "            background-color:  #1f8142;\n",
       "            color:  #f1f1f1;\n",
       "        }#T_bbcd6_row17_col6{\n",
       "            background-color:  #359c53;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row18_col1{\n",
       "            background-color:  #cbea9c;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row18_col3,#T_bbcd6_row19_col8{\n",
       "            background-color:  #dff3a8;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row18_col5,#T_bbcd6_row22_col1,#T_bbcd6_row24_col7{\n",
       "            background-color:  #edf8b1;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row18_col7{\n",
       "            background-color:  #fbfdcf;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row19_col0,#T_bbcd6_row19_col3{\n",
       "            background-color:  #7ec97b;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row19_col1,#T_bbcd6_row23_col7{\n",
       "            background-color:  #66bd70;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row19_col4{\n",
       "            background-color:  #9cd687;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row19_col9{\n",
       "            background-color:  #edf8b2;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row20_col5{\n",
       "            background-color:  #93d284;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row20_col6{\n",
       "            background-color:  #58b669;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row21_col5{\n",
       "            background-color:  #f7fcb9;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row21_col8{\n",
       "            background-color:  #b2df90;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row22_col2,#T_bbcd6_row24_col2{\n",
       "            background-color:  #fafdcc;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row22_col7{\n",
       "            background-color:  #fdfeda;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row23_col1{\n",
       "            background-color:  #30954f;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row23_col2{\n",
       "            background-color:  #61bb6d;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row23_col3{\n",
       "            background-color:  #3ea75a;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row23_col4{\n",
       "            background-color:  #51b365;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row23_col5{\n",
       "            background-color:  #4eb163;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row23_col6{\n",
       "            background-color:  #cfec9e;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row23_col8{\n",
       "            background-color:  #6dc073;\n",
       "            color:  #000000;\n",
       "        }#T_bbcd6_row23_col9{\n",
       "            background-color:  #006837;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_bbcd6_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MV</th>        <th class=\"col_heading level0 col1\" >MAD</th>        <th class=\"col_heading level0 col2\" >MSV</th>        <th class=\"col_heading level0 col3\" >FLPM</th>        <th class=\"col_heading level0 col4\" >SLPM</th>        <th class=\"col_heading level0 col5\" >CVaR</th>        <th class=\"col_heading level0 col6\" >EVaR</th>        <th class=\"col_heading level0 col7\" >CDaR</th>        <th class=\"col_heading level0 col8\" >UCI</th>        <th class=\"col_heading level0 col9\" >EDaR</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "                        <td id=\"T_bbcd6_row0_col0\" class=\"data row0 col0\" >2.40%</td>\n",
       "                        <td id=\"T_bbcd6_row0_col1\" class=\"data row0 col1\" >2.20%</td>\n",
       "                        <td id=\"T_bbcd6_row0_col2\" class=\"data row0 col2\" >2.50%</td>\n",
       "                        <td id=\"T_bbcd6_row0_col3\" class=\"data row0 col3\" >2.03%</td>\n",
       "                        <td id=\"T_bbcd6_row0_col4\" class=\"data row0 col4\" >2.40%</td>\n",
       "                        <td id=\"T_bbcd6_row0_col5\" class=\"data row0 col5\" >2.45%</td>\n",
       "                        <td id=\"T_bbcd6_row0_col6\" class=\"data row0 col6\" >2.80%</td>\n",
       "                        <td id=\"T_bbcd6_row0_col7\" class=\"data row0 col7\" >1.17%</td>\n",
       "                        <td id=\"T_bbcd6_row0_col8\" class=\"data row0 col8\" >1.16%</td>\n",
       "                        <td id=\"T_bbcd6_row0_col9\" class=\"data row0 col9\" >1.05%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "                        <td id=\"T_bbcd6_row1_col0\" class=\"data row1 col0\" >3.08%</td>\n",
       "                        <td id=\"T_bbcd6_row1_col1\" class=\"data row1 col1\" >3.23%</td>\n",
       "                        <td id=\"T_bbcd6_row1_col2\" class=\"data row1 col2\" >2.99%</td>\n",
       "                        <td id=\"T_bbcd6_row1_col3\" class=\"data row1 col3\" >3.30%</td>\n",
       "                        <td id=\"T_bbcd6_row1_col4\" class=\"data row1 col4\" >3.04%</td>\n",
       "                        <td id=\"T_bbcd6_row1_col5\" class=\"data row1 col5\" >2.86%</td>\n",
       "                        <td id=\"T_bbcd6_row1_col6\" class=\"data row1 col6\" >2.77%</td>\n",
       "                        <td id=\"T_bbcd6_row1_col7\" class=\"data row1 col7\" >6.44%</td>\n",
       "                        <td id=\"T_bbcd6_row1_col8\" class=\"data row1 col8\" >5.96%</td>\n",
       "                        <td id=\"T_bbcd6_row1_col9\" class=\"data row1 col9\" >4.23%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "                        <td id=\"T_bbcd6_row2_col0\" class=\"data row2 col0\" >4.03%</td>\n",
       "                        <td id=\"T_bbcd6_row2_col1\" class=\"data row2 col1\" >4.42%</td>\n",
       "                        <td id=\"T_bbcd6_row2_col2\" class=\"data row2 col2\" >3.93%</td>\n",
       "                        <td id=\"T_bbcd6_row2_col3\" class=\"data row2 col3\" >4.64%</td>\n",
       "                        <td id=\"T_bbcd6_row2_col4\" class=\"data row2 col4\" >4.02%</td>\n",
       "                        <td id=\"T_bbcd6_row2_col5\" class=\"data row2 col5\" >3.77%</td>\n",
       "                        <td id=\"T_bbcd6_row2_col6\" class=\"data row2 col6\" >3.49%</td>\n",
       "                        <td id=\"T_bbcd6_row2_col7\" class=\"data row2 col7\" >2.68%</td>\n",
       "                        <td id=\"T_bbcd6_row2_col8\" class=\"data row2 col8\" >3.42%</td>\n",
       "                        <td id=\"T_bbcd6_row2_col9\" class=\"data row2 col9\" >2.69%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "                        <td id=\"T_bbcd6_row3_col0\" class=\"data row3 col0\" >4.12%</td>\n",
       "                        <td id=\"T_bbcd6_row3_col1\" class=\"data row3 col1\" >4.24%</td>\n",
       "                        <td id=\"T_bbcd6_row3_col2\" class=\"data row3 col2\" >4.19%</td>\n",
       "                        <td id=\"T_bbcd6_row3_col3\" class=\"data row3 col3\" >4.06%</td>\n",
       "                        <td id=\"T_bbcd6_row3_col4\" class=\"data row3 col4\" >4.07%</td>\n",
       "                        <td id=\"T_bbcd6_row3_col5\" class=\"data row3 col5\" >3.83%</td>\n",
       "                        <td id=\"T_bbcd6_row3_col6\" class=\"data row3 col6\" >4.67%</td>\n",
       "                        <td id=\"T_bbcd6_row3_col7\" class=\"data row3 col7\" >2.01%</td>\n",
       "                        <td id=\"T_bbcd6_row3_col8\" class=\"data row3 col8\" >1.58%</td>\n",
       "                        <td id=\"T_bbcd6_row3_col9\" class=\"data row3 col9\" >2.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "                        <td id=\"T_bbcd6_row4_col0\" class=\"data row4 col0\" >3.89%</td>\n",
       "                        <td id=\"T_bbcd6_row4_col1\" class=\"data row4 col1\" >4.01%</td>\n",
       "                        <td id=\"T_bbcd6_row4_col2\" class=\"data row4 col2\" >3.89%</td>\n",
       "                        <td id=\"T_bbcd6_row4_col3\" class=\"data row4 col3\" >3.86%</td>\n",
       "                        <td id=\"T_bbcd6_row4_col4\" class=\"data row4 col4\" >3.90%</td>\n",
       "                        <td id=\"T_bbcd6_row4_col5\" class=\"data row4 col5\" >3.72%</td>\n",
       "                        <td id=\"T_bbcd6_row4_col6\" class=\"data row4 col6\" >3.52%</td>\n",
       "                        <td id=\"T_bbcd6_row4_col7\" class=\"data row4 col7\" >2.66%</td>\n",
       "                        <td id=\"T_bbcd6_row4_col8\" class=\"data row4 col8\" >2.50%</td>\n",
       "                        <td id=\"T_bbcd6_row4_col9\" class=\"data row4 col9\" >3.59%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "                        <td id=\"T_bbcd6_row5_col0\" class=\"data row5 col0\" >5.20%</td>\n",
       "                        <td id=\"T_bbcd6_row5_col1\" class=\"data row5 col1\" >5.10%</td>\n",
       "                        <td id=\"T_bbcd6_row5_col2\" class=\"data row5 col2\" >5.22%</td>\n",
       "                        <td id=\"T_bbcd6_row5_col3\" class=\"data row5 col3\" >5.12%</td>\n",
       "                        <td id=\"T_bbcd6_row5_col4\" class=\"data row5 col4\" >5.34%</td>\n",
       "                        <td id=\"T_bbcd6_row5_col5\" class=\"data row5 col5\" >5.63%</td>\n",
       "                        <td id=\"T_bbcd6_row5_col6\" class=\"data row5 col6\" >5.50%</td>\n",
       "                        <td id=\"T_bbcd6_row5_col7\" class=\"data row5 col7\" >7.25%</td>\n",
       "                        <td id=\"T_bbcd6_row5_col8\" class=\"data row5 col8\" >5.71%</td>\n",
       "                        <td id=\"T_bbcd6_row5_col9\" class=\"data row5 col9\" >9.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "                        <td id=\"T_bbcd6_row6_col0\" class=\"data row6 col0\" >5.10%</td>\n",
       "                        <td id=\"T_bbcd6_row6_col1\" class=\"data row6 col1\" >4.53%</td>\n",
       "                        <td id=\"T_bbcd6_row6_col2\" class=\"data row6 col2\" >5.70%</td>\n",
       "                        <td id=\"T_bbcd6_row6_col3\" class=\"data row6 col3\" >4.50%</td>\n",
       "                        <td id=\"T_bbcd6_row6_col4\" class=\"data row6 col4\" >5.66%</td>\n",
       "                        <td id=\"T_bbcd6_row6_col5\" class=\"data row6 col5\" >6.96%</td>\n",
       "                        <td id=\"T_bbcd6_row6_col6\" class=\"data row6 col6\" >7.73%</td>\n",
       "                        <td id=\"T_bbcd6_row6_col7\" class=\"data row6 col7\" >2.85%</td>\n",
       "                        <td id=\"T_bbcd6_row6_col8\" class=\"data row6 col8\" >2.52%</td>\n",
       "                        <td id=\"T_bbcd6_row6_col9\" class=\"data row6 col9\" >2.65%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "                        <td id=\"T_bbcd6_row7_col0\" class=\"data row7 col0\" >3.04%</td>\n",
       "                        <td id=\"T_bbcd6_row7_col1\" class=\"data row7 col1\" >3.04%</td>\n",
       "                        <td id=\"T_bbcd6_row7_col2\" class=\"data row7 col2\" >2.91%</td>\n",
       "                        <td id=\"T_bbcd6_row7_col3\" class=\"data row7 col3\" >3.08%</td>\n",
       "                        <td id=\"T_bbcd6_row7_col4\" class=\"data row7 col4\" >2.94%</td>\n",
       "                        <td id=\"T_bbcd6_row7_col5\" class=\"data row7 col5\" >2.79%</td>\n",
       "                        <td id=\"T_bbcd6_row7_col6\" class=\"data row7 col6\" >2.95%</td>\n",
       "                        <td id=\"T_bbcd6_row7_col7\" class=\"data row7 col7\" >5.21%</td>\n",
       "                        <td id=\"T_bbcd6_row7_col8\" class=\"data row7 col8\" >5.29%</td>\n",
       "                        <td id=\"T_bbcd6_row7_col9\" class=\"data row7 col9\" >7.13%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "                        <td id=\"T_bbcd6_row8_col0\" class=\"data row8 col0\" >2.83%</td>\n",
       "                        <td id=\"T_bbcd6_row8_col1\" class=\"data row8 col1\" >2.95%</td>\n",
       "                        <td id=\"T_bbcd6_row8_col2\" class=\"data row8 col2\" >2.64%</td>\n",
       "                        <td id=\"T_bbcd6_row8_col3\" class=\"data row8 col3\" >2.91%</td>\n",
       "                        <td id=\"T_bbcd6_row8_col4\" class=\"data row8 col4\" >2.62%</td>\n",
       "                        <td id=\"T_bbcd6_row8_col5\" class=\"data row8 col5\" >2.36%</td>\n",
       "                        <td id=\"T_bbcd6_row8_col6\" class=\"data row8 col6\" >2.69%</td>\n",
       "                        <td id=\"T_bbcd6_row8_col7\" class=\"data row8 col7\" >2.56%</td>\n",
       "                        <td id=\"T_bbcd6_row8_col8\" class=\"data row8 col8\" >2.56%</td>\n",
       "                        <td id=\"T_bbcd6_row8_col9\" class=\"data row8 col9\" >2.43%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "                        <td id=\"T_bbcd6_row9_col0\" class=\"data row9 col0\" >3.61%</td>\n",
       "                        <td id=\"T_bbcd6_row9_col1\" class=\"data row9 col1\" >3.52%</td>\n",
       "                        <td id=\"T_bbcd6_row9_col2\" class=\"data row9 col2\" >3.60%</td>\n",
       "                        <td id=\"T_bbcd6_row9_col3\" class=\"data row9 col3\" >3.49%</td>\n",
       "                        <td id=\"T_bbcd6_row9_col4\" class=\"data row9 col4\" >3.58%</td>\n",
       "                        <td id=\"T_bbcd6_row9_col5\" class=\"data row9 col5\" >3.38%</td>\n",
       "                        <td id=\"T_bbcd6_row9_col6\" class=\"data row9 col6\" >3.27%</td>\n",
       "                        <td id=\"T_bbcd6_row9_col7\" class=\"data row9 col7\" >1.91%</td>\n",
       "                        <td id=\"T_bbcd6_row9_col8\" class=\"data row9 col8\" >2.04%</td>\n",
       "                        <td id=\"T_bbcd6_row9_col9\" class=\"data row9 col9\" >1.97%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "                        <td id=\"T_bbcd6_row10_col0\" class=\"data row10 col0\" >3.35%</td>\n",
       "                        <td id=\"T_bbcd6_row10_col1\" class=\"data row10 col1\" >3.68%</td>\n",
       "                        <td id=\"T_bbcd6_row10_col2\" class=\"data row10 col2\" >3.35%</td>\n",
       "                        <td id=\"T_bbcd6_row10_col3\" class=\"data row10 col3\" >3.88%</td>\n",
       "                        <td id=\"T_bbcd6_row10_col4\" class=\"data row10 col4\" >3.40%</td>\n",
       "                        <td id=\"T_bbcd6_row10_col5\" class=\"data row10 col5\" >2.98%</td>\n",
       "                        <td id=\"T_bbcd6_row10_col6\" class=\"data row10 col6\" >3.08%</td>\n",
       "                        <td id=\"T_bbcd6_row10_col7\" class=\"data row10 col7\" >3.69%</td>\n",
       "                        <td id=\"T_bbcd6_row10_col8\" class=\"data row10 col8\" >3.73%</td>\n",
       "                        <td id=\"T_bbcd6_row10_col9\" class=\"data row10 col9\" >3.17%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "                        <td id=\"T_bbcd6_row11_col0\" class=\"data row11 col0\" >3.42%</td>\n",
       "                        <td id=\"T_bbcd6_row11_col1\" class=\"data row11 col1\" >3.36%</td>\n",
       "                        <td id=\"T_bbcd6_row11_col2\" class=\"data row11 col2\" >3.33%</td>\n",
       "                        <td id=\"T_bbcd6_row11_col3\" class=\"data row11 col3\" >3.13%</td>\n",
       "                        <td id=\"T_bbcd6_row11_col4\" class=\"data row11 col4\" >3.28%</td>\n",
       "                        <td id=\"T_bbcd6_row11_col5\" class=\"data row11 col5\" >2.92%</td>\n",
       "                        <td id=\"T_bbcd6_row11_col6\" class=\"data row11 col6\" >3.80%</td>\n",
       "                        <td id=\"T_bbcd6_row11_col7\" class=\"data row11 col7\" >1.98%</td>\n",
       "                        <td id=\"T_bbcd6_row11_col8\" class=\"data row11 col8\" >1.82%</td>\n",
       "                        <td id=\"T_bbcd6_row11_col9\" class=\"data row11 col9\" >1.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "                        <td id=\"T_bbcd6_row12_col0\" class=\"data row12 col0\" >4.41%</td>\n",
       "                        <td id=\"T_bbcd6_row12_col1\" class=\"data row12 col1\" >4.69%</td>\n",
       "                        <td id=\"T_bbcd6_row12_col2\" class=\"data row12 col2\" >4.33%</td>\n",
       "                        <td id=\"T_bbcd6_row12_col3\" class=\"data row12 col3\" >5.03%</td>\n",
       "                        <td id=\"T_bbcd6_row12_col4\" class=\"data row12 col4\" >4.44%</td>\n",
       "                        <td id=\"T_bbcd6_row12_col5\" class=\"data row12 col5\" >4.09%</td>\n",
       "                        <td id=\"T_bbcd6_row12_col6\" class=\"data row12 col6\" >4.36%</td>\n",
       "                        <td id=\"T_bbcd6_row12_col7\" class=\"data row12 col7\" >5.65%</td>\n",
       "                        <td id=\"T_bbcd6_row12_col8\" class=\"data row12 col8\" >6.52%</td>\n",
       "                        <td id=\"T_bbcd6_row12_col9\" class=\"data row12 col9\" >5.05%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "                        <td id=\"T_bbcd6_row13_col0\" class=\"data row13 col0\" >5.18%</td>\n",
       "                        <td id=\"T_bbcd6_row13_col1\" class=\"data row13 col1\" >5.04%</td>\n",
       "                        <td id=\"T_bbcd6_row13_col2\" class=\"data row13 col2\" >4.98%</td>\n",
       "                        <td id=\"T_bbcd6_row13_col3\" class=\"data row13 col3\" >4.83%</td>\n",
       "                        <td id=\"T_bbcd6_row13_col4\" class=\"data row13 col4\" >4.83%</td>\n",
       "                        <td id=\"T_bbcd6_row13_col5\" class=\"data row13 col5\" >5.00%</td>\n",
       "                        <td id=\"T_bbcd6_row13_col6\" class=\"data row13 col6\" >4.04%</td>\n",
       "                        <td id=\"T_bbcd6_row13_col7\" class=\"data row13 col7\" >2.28%</td>\n",
       "                        <td id=\"T_bbcd6_row13_col8\" class=\"data row13 col8\" >2.01%</td>\n",
       "                        <td id=\"T_bbcd6_row13_col9\" class=\"data row13 col9\" >2.14%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "                        <td id=\"T_bbcd6_row14_col0\" class=\"data row14 col0\" >3.26%</td>\n",
       "                        <td id=\"T_bbcd6_row14_col1\" class=\"data row14 col1\" >3.89%</td>\n",
       "                        <td id=\"T_bbcd6_row14_col2\" class=\"data row14 col2\" >3.25%</td>\n",
       "                        <td id=\"T_bbcd6_row14_col3\" class=\"data row14 col3\" >4.27%</td>\n",
       "                        <td id=\"T_bbcd6_row14_col4\" class=\"data row14 col4\" >3.34%</td>\n",
       "                        <td id=\"T_bbcd6_row14_col5\" class=\"data row14 col5\" >2.94%</td>\n",
       "                        <td id=\"T_bbcd6_row14_col6\" class=\"data row14 col6\" >3.15%</td>\n",
       "                        <td id=\"T_bbcd6_row14_col7\" class=\"data row14 col7\" >6.06%</td>\n",
       "                        <td id=\"T_bbcd6_row14_col8\" class=\"data row14 col8\" >9.18%</td>\n",
       "                        <td id=\"T_bbcd6_row14_col9\" class=\"data row14 col9\" >4.43%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "                        <td id=\"T_bbcd6_row15_col0\" class=\"data row15 col0\" >6.54%</td>\n",
       "                        <td id=\"T_bbcd6_row15_col1\" class=\"data row15 col1\" >6.48%</td>\n",
       "                        <td id=\"T_bbcd6_row15_col2\" class=\"data row15 col2\" >6.63%</td>\n",
       "                        <td id=\"T_bbcd6_row15_col3\" class=\"data row15 col3\" >6.99%</td>\n",
       "                        <td id=\"T_bbcd6_row15_col4\" class=\"data row15 col4\" >6.78%</td>\n",
       "                        <td id=\"T_bbcd6_row15_col5\" class=\"data row15 col5\" >6.70%</td>\n",
       "                        <td id=\"T_bbcd6_row15_col6\" class=\"data row15 col6\" >6.40%</td>\n",
       "                        <td id=\"T_bbcd6_row15_col7\" class=\"data row15 col7\" >5.84%</td>\n",
       "                        <td id=\"T_bbcd6_row15_col8\" class=\"data row15 col8\" >7.06%</td>\n",
       "                        <td id=\"T_bbcd6_row15_col9\" class=\"data row15 col9\" >5.50%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "                        <td id=\"T_bbcd6_row16_col0\" class=\"data row16 col0\" >2.99%</td>\n",
       "                        <td id=\"T_bbcd6_row16_col1\" class=\"data row16 col1\" >2.81%</td>\n",
       "                        <td id=\"T_bbcd6_row16_col2\" class=\"data row16 col2\" >3.04%</td>\n",
       "                        <td id=\"T_bbcd6_row16_col3\" class=\"data row16 col3\" >2.81%</td>\n",
       "                        <td id=\"T_bbcd6_row16_col4\" class=\"data row16 col4\" >3.06%</td>\n",
       "                        <td id=\"T_bbcd6_row16_col5\" class=\"data row16 col5\" >3.25%</td>\n",
       "                        <td id=\"T_bbcd6_row16_col6\" class=\"data row16 col6\" >3.23%</td>\n",
       "                        <td id=\"T_bbcd6_row16_col7\" class=\"data row16 col7\" >1.90%</td>\n",
       "                        <td id=\"T_bbcd6_row16_col8\" class=\"data row16 col8\" >1.92%</td>\n",
       "                        <td id=\"T_bbcd6_row16_col9\" class=\"data row16 col9\" >2.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "                        <td id=\"T_bbcd6_row17_col0\" class=\"data row17 col0\" >6.87%</td>\n",
       "                        <td id=\"T_bbcd6_row17_col1\" class=\"data row17 col1\" >6.30%</td>\n",
       "                        <td id=\"T_bbcd6_row17_col2\" class=\"data row17 col2\" >7.30%</td>\n",
       "                        <td id=\"T_bbcd6_row17_col3\" class=\"data row17 col3\" >5.81%</td>\n",
       "                        <td id=\"T_bbcd6_row17_col4\" class=\"data row17 col4\" >7.14%</td>\n",
       "                        <td id=\"T_bbcd6_row17_col5\" class=\"data row17 col5\" >7.97%</td>\n",
       "                        <td id=\"T_bbcd6_row17_col6\" class=\"data row17 col6\" >6.09%</td>\n",
       "                        <td id=\"T_bbcd6_row17_col7\" class=\"data row17 col7\" >13.43%</td>\n",
       "                        <td id=\"T_bbcd6_row17_col8\" class=\"data row17 col8\" >10.96%</td>\n",
       "                        <td id=\"T_bbcd6_row17_col9\" class=\"data row17 col9\" >12.55%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "                        <td id=\"T_bbcd6_row18_col0\" class=\"data row18 col0\" >3.54%</td>\n",
       "                        <td id=\"T_bbcd6_row18_col1\" class=\"data row18 col1\" >3.45%</td>\n",
       "                        <td id=\"T_bbcd6_row18_col2\" class=\"data row18 col2\" >3.38%</td>\n",
       "                        <td id=\"T_bbcd6_row18_col3\" class=\"data row18 col3\" >3.15%</td>\n",
       "                        <td id=\"T_bbcd6_row18_col4\" class=\"data row18 col4\" >3.25%</td>\n",
       "                        <td id=\"T_bbcd6_row18_col5\" class=\"data row18 col5\" >3.32%</td>\n",
       "                        <td id=\"T_bbcd6_row18_col6\" class=\"data row18 col6\" >3.58%</td>\n",
       "                        <td id=\"T_bbcd6_row18_col7\" class=\"data row18 col7\" >1.97%</td>\n",
       "                        <td id=\"T_bbcd6_row18_col8\" class=\"data row18 col8\" >1.83%</td>\n",
       "                        <td id=\"T_bbcd6_row18_col9\" class=\"data row18 col9\" >2.36%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "                        <td id=\"T_bbcd6_row19_col0\" class=\"data row19 col0\" >4.58%</td>\n",
       "                        <td id=\"T_bbcd6_row19_col1\" class=\"data row19 col1\" >4.52%</td>\n",
       "                        <td id=\"T_bbcd6_row19_col2\" class=\"data row19 col2\" >4.41%</td>\n",
       "                        <td id=\"T_bbcd6_row19_col3\" class=\"data row19 col3\" >4.43%</td>\n",
       "                        <td id=\"T_bbcd6_row19_col4\" class=\"data row19 col4\" >4.37%</td>\n",
       "                        <td id=\"T_bbcd6_row19_col5\" class=\"data row19 col5\" >4.30%</td>\n",
       "                        <td id=\"T_bbcd6_row19_col6\" class=\"data row19 col6\" >4.03%</td>\n",
       "                        <td id=\"T_bbcd6_row19_col7\" class=\"data row19 col7\" >3.30%</td>\n",
       "                        <td id=\"T_bbcd6_row19_col8\" class=\"data row19 col8\" >3.34%</td>\n",
       "                        <td id=\"T_bbcd6_row19_col9\" class=\"data row19 col9\" >2.95%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "                        <td id=\"T_bbcd6_row20_col0\" class=\"data row20 col0\" >3.97%</td>\n",
       "                        <td id=\"T_bbcd6_row20_col1\" class=\"data row20 col1\" >3.64%</td>\n",
       "                        <td id=\"T_bbcd6_row20_col2\" class=\"data row20 col2\" >4.19%</td>\n",
       "                        <td id=\"T_bbcd6_row20_col3\" class=\"data row20 col3\" >3.87%</td>\n",
       "                        <td id=\"T_bbcd6_row20_col4\" class=\"data row20 col4\" >4.34%</td>\n",
       "                        <td id=\"T_bbcd6_row20_col5\" class=\"data row20 col5\" >4.80%</td>\n",
       "                        <td id=\"T_bbcd6_row20_col6\" class=\"data row20 col6\" >5.59%</td>\n",
       "                        <td id=\"T_bbcd6_row20_col7\" class=\"data row20 col7\" >2.01%</td>\n",
       "                        <td id=\"T_bbcd6_row20_col8\" class=\"data row20 col8\" >2.23%</td>\n",
       "                        <td id=\"T_bbcd6_row20_col9\" class=\"data row20 col9\" >1.74%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "                        <td id=\"T_bbcd6_row21_col0\" class=\"data row21 col0\" >3.43%</td>\n",
       "                        <td id=\"T_bbcd6_row21_col1\" class=\"data row21 col1\" >3.62%</td>\n",
       "                        <td id=\"T_bbcd6_row21_col2\" class=\"data row21 col2\" >3.36%</td>\n",
       "                        <td id=\"T_bbcd6_row21_col3\" class=\"data row21 col3\" >3.73%</td>\n",
       "                        <td id=\"T_bbcd6_row21_col4\" class=\"data row21 col4\" >3.41%</td>\n",
       "                        <td id=\"T_bbcd6_row21_col5\" class=\"data row21 col5\" >3.08%</td>\n",
       "                        <td id=\"T_bbcd6_row21_col6\" class=\"data row21 col6\" >3.25%</td>\n",
       "                        <td id=\"T_bbcd6_row21_col7\" class=\"data row21 col7\" >4.56%</td>\n",
       "                        <td id=\"T_bbcd6_row21_col8\" class=\"data row21 col8\" >4.71%</td>\n",
       "                        <td id=\"T_bbcd6_row21_col9\" class=\"data row21 col9\" >4.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "                        <td id=\"T_bbcd6_row22_col0\" class=\"data row22 col0\" >2.95%</td>\n",
       "                        <td id=\"T_bbcd6_row22_col1\" class=\"data row22 col1\" >2.92%</td>\n",
       "                        <td id=\"T_bbcd6_row22_col2\" class=\"data row22 col2\" >2.86%</td>\n",
       "                        <td id=\"T_bbcd6_row22_col3\" class=\"data row22 col3\" >2.68%</td>\n",
       "                        <td id=\"T_bbcd6_row22_col4\" class=\"data row22 col4\" >2.75%</td>\n",
       "                        <td id=\"T_bbcd6_row22_col5\" class=\"data row22 col5\" >2.66%</td>\n",
       "                        <td id=\"T_bbcd6_row22_col6\" class=\"data row22 col6\" >3.12%</td>\n",
       "                        <td id=\"T_bbcd6_row22_col7\" class=\"data row22 col7\" >1.58%</td>\n",
       "                        <td id=\"T_bbcd6_row22_col8\" class=\"data row22 col8\" >1.58%</td>\n",
       "                        <td id=\"T_bbcd6_row22_col9\" class=\"data row22 col9\" >1.37%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "                        <td id=\"T_bbcd6_row23_col0\" class=\"data row23 col0\" >5.19%</td>\n",
       "                        <td id=\"T_bbcd6_row23_col1\" class=\"data row23 col1\" >5.16%</td>\n",
       "                        <td id=\"T_bbcd6_row23_col2\" class=\"data row23 col2\" >5.15%</td>\n",
       "                        <td id=\"T_bbcd6_row23_col3\" class=\"data row23 col3\" >5.19%</td>\n",
       "                        <td id=\"T_bbcd6_row23_col4\" class=\"data row23 col4\" >5.19%</td>\n",
       "                        <td id=\"T_bbcd6_row23_col5\" class=\"data row23 col5\" >5.70%</td>\n",
       "                        <td id=\"T_bbcd6_row23_col6\" class=\"data row23 col6\" >4.10%</td>\n",
       "                        <td id=\"T_bbcd6_row23_col7\" class=\"data row23 col7\" >7.78%</td>\n",
       "                        <td id=\"T_bbcd6_row23_col8\" class=\"data row23 col8\" >6.32%</td>\n",
       "                        <td id=\"T_bbcd6_row23_col9\" class=\"data row23 col9\" >11.11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_bbcd6_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "                        <td id=\"T_bbcd6_row24_col0\" class=\"data row24 col0\" >3.00%</td>\n",
       "                        <td id=\"T_bbcd6_row24_col1\" class=\"data row24 col1\" >3.20%</td>\n",
       "                        <td id=\"T_bbcd6_row24_col2\" class=\"data row24 col2\" >2.86%</td>\n",
       "                        <td id=\"T_bbcd6_row24_col3\" class=\"data row24 col3\" >3.21%</td>\n",
       "                        <td id=\"T_bbcd6_row24_col4\" class=\"data row24 col4\" >2.85%</td>\n",
       "                        <td id=\"T_bbcd6_row24_col5\" class=\"data row24 col5\" >2.54%</td>\n",
       "                        <td id=\"T_bbcd6_row24_col6\" class=\"data row24 col6\" >2.79%</td>\n",
       "                        <td id=\"T_bbcd6_row24_col7\" class=\"data row24 col7\" >3.25%</td>\n",
       "                        <td id=\"T_bbcd6_row24_col8\" class=\"data row24 col8\" >4.06%</td>\n",
       "                        <td id=\"T_bbcd6_row24_col9\" class=\"data row24 col9\" >2.47%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ff6e555aca0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(16)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
